Development of a bio-inspired vision system for mobile micro-robots

In this paper, we present a new bio-inspired vision system for mobile micro-robots. The processing method takes inspiration from vision of locusts in detecting the fast approaching objects. Research suggested that locusts use wide field visual neuron called the lobula giant movement detector to respond to imminent collisions. We employed the locusts' vision mechanism to motion control of a mobile robot. The selected image processing method is implemented on a developed extension module using a low-cost and fast ARM processor. The vision module is placed on top of a micro-robot to control its trajectory and to avoid obstacles. The observed results from several performed experiments demonstrated that the developed extension module and the inspired vision system are feasible to employ as a vision module for obstacle avoidance and motion control

Using a 3DOF Parallel Robot and a Spherical Bat to hit a Ping-Pong Ball

Playing the game of Ping-Pong is a challenge to human abilities since it requires developing skills, such as fast reaction capabilities, precision of movement and high speed mental responses. These processes include the utilization of seven DOF of the human arm, and translational movements through the legs, torso, and other extremities of the body, which are used for developing different game strategies or simply imposing movements that affect the ball such as spinning movements. Computationally, Ping-Pong requires a huge quantity of joints and visual information to be processed and analysed, something which really represents a challenge for a robot. In addition, in order for a robot to develop the task mechanically, it requires a large and dexterous workspace, and good dynamic capacities. Although there are commercial robots that are able to play Ping-Pong, the game is still an open task, where there are problems to be solved and simplified. All robotic Ping-Pong players cited in the bibliography used at least four DOF to hit the ball. In this paper, a spherical bat mounted on a 3-DOF parallel robot is proposed. The spherical bat is used to drive the trajectory of a Ping-Pong ball.Fil: Trasloheros, Alberto. Universidad Aeronáutica de Querétaro; MéxicoFil: Sebastián, José María. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Torrijos, Jesús. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Deep Predictive Policy Training using Reinforcement Learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017

Multiview System for Tracking a Fast Moving Object Against Complex Backgrounds

Tracking the real world coordinate of a fast moving object against a complex background is very challenging. When designing a multi-view system for this purpose, one key consideration is the arrangement of the cameras such that the object can be constantly and accurately tracked. This paper discusses a novel cameras arrangement, which can provide redundancy for fault tolerance, yet do not require installing more cameras nor relying aerial views of the scene. Using a table tennis match as example, experiment results show that the multi-view system with this cameras arrangement has a promising potential for tracking a table tennis ball in a real match scene

Classification of table tennis strokes using a wearable device and deep learning

The analysis of sports using everyday mobile devices is an area that has been increasingly explored aiming to help the user to improve in all aspects of the sport. The objective of the work proposed for this dissertation is to developed application capable of detecting strokes in table tennis using the iPhone and the Apple Watch, in which a recorded table tennis strokes data set performed by several table tennis athletes was created to help develop the application. Since the Artificial Intelegence area is increasingly present in our daily lives, the motivation in this work is to have a first contact with the current state of AI, the technologies available and most used in today’s present, and as within the company, it was intended to begin research in this area, mainly using Apple devices, it was decided to try and create a mobile application capable of detecting strokes performed in table tennis that would work with devices capable of AI processing, in order to provide statistical data to help table tennis athletes and coaches, which can later be sell for. After a study of devices available on the apple market with the necessary capabilities for the purpose of the work, it was concluded that for this work, the devices to be used would be the iPhone (above the X model) and the Apple Watch (above the model 5). Also because there were no public table tennis data set available, a methodology was developed with the objective of capturing table tennis strokes trough motion data. The recording of motion data was done by using an application capable of recording sensors data using the apple watch who was used by each athlete on the wrist. The sensors used to record motion data were accelerometer and gyroscope, and the capture methodology was planned and overseen by coaches and athletes. From the methodology created, 2 base data sets were created. One consisting of a short interval between strokes and the second and last with a bigger interval between strokes. From these 2 data sets, 3 more were created with different pre processing configurations applied followed by a filtering and reformatting of data to the necessary format for the creation of a Deep Learning model. To generate a DL classifier model, two approaches were tested, one by using Create ML, and the other by using Convolution Neural Network-Long Short Term Memory and Convolution Neural Network-Long Short Term Memory architecture. To evaluate the models, statistics generated from training were saved during model testing and creation. Create ML data set classifier models showed average performance except in one data set, with the generated classifier model having a maximum performance of 89.66% F1 score while CNN-LSTM and ConvLSTM approach generated good performance from all data set generated classifier models with the best classifier being the ConvLSTM with a 97.33% F1 score. After the creation of this same model, development of the application was performed consisting of two parts, one on the iPhone where it is possible to see the statistics and another on the Apple Watch where the ML model is executed and the stroke performed is detected being then sent to the application on the iPhone. The final step consisted on evaluation of the application during a live game scenario followed by an user rating application feedback questionnaire on athletes and coaches. Final application feedback was positive across all subjects with recommendations to the application interface and improvements to the classifier model. The live game application scenario with the generated classifier model obtained a 80% correct labelled strokes

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks